scholarly journals Auction design for cross-edge task offloading in heterogeneous mobile edge clouds

2021 ◽  
Author(s):  
Weifeng Lu ◽  
Weiduo Wu ◽  
Jia Xu ◽  
Pengcheng Zhao ◽  
Dejun Yang ◽  
...  
Keyword(s):  
Auction Design ◽  
Task Offloading

Forestry: The Mechanisms of the Accordance of the Rights of User Analysis

2006 ◽  
pp. 57-70 ◽  
Author(s):  
A. Manakov
Keyword(s):  
International Experience ◽  
Auction Design

The author considers the ways in which the rights of user of the forestry in Russia are granted. The article analyzes the international experience of forest auctions and describes the main problems of the auction design.

Optimal Auction Design with Deferred Inspection and Reward

2020 ◽  
Author(s):  
Saeed Alaei ◽  
Alexandre Belloni ◽  
Ali Makhdoumi ◽  
Azarakhsh Malekian
Keyword(s):  
Auction Design ◽  
Optimal Auction

scholarly journals Computation offloading through mobile vehicles in IoT-edge-cloud network

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Jun Long ◽  
Yueyi Luo ◽  
Xiaoyu Zhu ◽  
Entao Luo ◽  
Mingfeng Huang
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Low Cost ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Challenging Problem ◽  
Learning Technique ◽  
Cloud Network ◽  
The City ◽  
Task Offloading

AbstractWith the developing of Internet of Things (IoT) and mobile edge computing (MEC), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

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